Research on intelligent generation of structural demolition suggestions based on multi-model collaboration

TL;DR

This paper introduces an intelligent, multi-model collaborative framework that enhances the automatic generation of structural demolition suggestions, making the process more efficient and tailored to specific engineering cases.

Contribution

It develops a novel multi-model collaboration approach utilizing Retrieval-Augmented Generation and Low-Rank Adaptation Fine-Tuning to improve large language model performance in structural demolition planning.

Findings

Enhanced suggestion relevance compared to CivilGPT

Improved focus on key structural information

More targeted demolition recommendations

Abstract

The steel structure demolition scheme needs to be compiled according to the specific engineering characteristics and the update results of the finite element model. The designers need to refer to the relevant engineering cases according to the standard requirements when compiling. It takes a lot of time to retrieve information and organize language, and the degree of automation and intelligence is low. This paper proposes an intelligent generation method of structural demolition suggestions based on multi-model collaboration, and improves the text generation performance of large language models in the field of structural demolition by Retrieval-Augmented Generation and Low-Rank Adaptation Fine-Tuning technology. The intelligent generation framework of multi-model collaborative structural demolition suggestions can start from the specific engineering situation, drive the large language model to answer with anthropomorphic thinking, and propose demolition suggestions that are highly consistent with the characteristics of the structure. Compared with CivilGPT, the multi-model collaboration framework proposed in this paper can focus more on the key information of the structure, and the suggestions are more targeted.